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Digital twin-based degradation prediction for train electro-pneumatic valve

Author

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  • Xiong, Liujing
  • He, Yifan
  • Chen, Yuejian
  • Lu, Jinjun
  • Niu, Gang

Abstract

The train electro-pneumatic (EP) valve is a crucial driving component in the electronically-controlled pneumatic (ECP) brake system and directly affects the reliability of brake equipment. The degradation prediction of the EP valve can help to ensure the safe and reliable operation of the ECP brake system and enable predictive maintenance-based decision-making. Yet, degradation prediction of the EP valve faces several challenges such as insufficient fault data and complex physics. This paper proposes a degradation prediction scheme based on a high-fidelity digital twin (DT) model with parameter-updating ability, consisting of the EP valve model and the degradation model of the direct-current resistance (DCR). The electromagnetic part and the pneumatic part were constructed to accurately simulate the dynamic response of the EP valve. Moreover, a novel degradation model with adaptive forgetting factors is developed to regularly update the EP valve model. This approach can improve prediction accuracy and continuously track the latest degradation trend through the information interaction between virtual simulations and actual experiments. The effectiveness of the proposed scheme was demonstrated in a case study through the accelerated degradation tests and brake test-rig experiments.

Suggested Citation

  • Xiong, Liujing & He, Yifan & Chen, Yuejian & Lu, Jinjun & Niu, Gang, 2023. "Digital twin-based degradation prediction for train electro-pneumatic valve," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023005410
    DOI: 10.1016/j.ress.2023.109627
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    Cited by:

    1. Tao, Haohan & Jia, Peng & Wang, Xiangyu & Wang, Liquan, 2024. "Reliability analysis of subsea control module based on dynamic Bayesian network and digital twin," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    2. Huang, Keke & Tao, Shijun & Wu, Dehao & Yang, Chunhua & Gui, Weihua, 2024. "Robust condition identification against label noise in industrial processes based on trusted connection dictionary learning," Reliability Engineering and System Safety, Elsevier, vol. 247(C).

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